# Simplification of General Mixed Boolean-Arithmetic Expressions: GAMBA

arXiv: https://arxiv.org/abs/2305.06763 · HTML: https://mlsanity.com/paper/2305.06763/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2023.

## Built on by (most cited first, 2 of 2)

- [gMBA: Expression Semantic Guided Mixed Boolean-Arithmetic Deobfuscation Using Transformer Architectures](https://mlsanity.com/paper/2506.23634/) (2025)
- [GPU-Accelerated Synthesis of Mixed-Boolean Arithmetic: Beyond Caching](https://mlsanity.com/paper/2605.08243/) (2026)

## Abstract (arXiv)

Malware code often resorts to various self-protection techniques to complicate analysis. One such technique is applying Mixed-Boolean Arithmetic (MBA) expressions as a way to create opaque predicates and diversify and obfuscate the data flow. In this work we aim to provide tools for the simplification of nonlinear MBA expressions in a very practical context to compete in the arms race between the generation of hard, diverse MBAs and their analysis. The proposed algorithm GAMBA employs algebraic rewriting at its core and extends SiMBA. It achieves efficient deobfuscation of MBA expressions from the most widely tested public datasets and simplifies expressions to their ground truths in most cases, surpassing peer tools.